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††*Corresponding author: hshahria@kennesaw.edu

Evolution of Quantum Computing: A Systematic

Survey on the Use of Quantum Computing Tools

Paramita Basak Upama

∗

, Md Jobair Hossain Faruk

†

, Mohammad Nazim

‡

, Mohammad Masum

§

, Hossain Shahriar

§

Gias Uddin

¶

, Shabir Barzanjeh

‡‡

, Sheikh Iqbal Ahamed

∗

, Akond Rahman

‖

∗Department)of)Computer)Science,)Marquette)University,)USA)

†Department of Software Engineering and Game Development, Kennesaw State University, USA

‡Department Computer Science; Kennesaw State University, USA

§Department of Information Technology, Kennesaw State University, USA

¶Schulich School of Engineering, University of Calgary, Canada

‡‡Department of Physics and Astronomy, University of Calgary, Canada

‖

Department of Computer Science, Tennessee Tech university, USA

{

∗paramitabasak.upama

}@marquette.edu,){

†mhossa21,)‡mnazim

}@students.kennesaw.edu,){

§hshahria,)mmasum

}@kennesaw.edu,)

{

¶gias.uddin,)‡‡shabir.barzanjeh

}@ucalgary.ca,)){

*sheikh.ahamed

}@marquette.edu,))))&)))){

‡arahman

}@tntech.edu)

Abstract—Quantum Computing (QC) refers to an emerging

paradigm that inherits and builds with the concepts and

phenomena of Quantum Mechanic (QM) with the significant

potential to unlock a remarkable opportunity to solve complex and

computationally intractable problems that scientists could not

tackle previously. In recent years, tremendous efforts and

progress in QC mark a significant milestone in solving real-world

problems much more efficiently than classical computing

technology. While considerable progress is being made to move

quantum computing in recent years, significant research efforts

need to be devoted to move this domain from an idea to a working

paradigm. In this paper, we conduct a systematic survey and

categorize papers, tools, frameworks, platforms that facilitate

quantum computing and analyze them from an application and

Quantum Computing perspective. We present quantum

Computing Layers, Characteristics of Quantum Computer

platforms, Circuit Simulator, Open-source Tools- Cirq,

TensorFlow Quantum, ProjectQ etc. that allow implementing

quantum programs in Python using a powerful and intuitive

syntax. Following that, we discuss the current essence, identify

open challenges, and provide future research direction. We

conclude that scores of frameworks, tools and platforms are

emerged in the past few years, improvement of currently available

facilities would exploit the research activities in the quantum

research community.

Keywords—Quantum Computing, Qubits, Quantum Sensing,

Platforms and Tools for Quantum Computing, Evolution of

Quantum Computing

I. INTRODUCTION

Quantum computing relies on properties of quantum

mechanics to compute problems that are beyond the reach of the

existing classical computers and achieved significant

advancements in the past few years [1], [2]. Intersecting of

various techniques including physics, mathematics, computer

science, and information theory paved to initiate a perfect

domain, quantum computing capable of performing calculations

deemed unachievable for classical computers. Compared to

classical computers, quantum computers have high

computational power, less energy consumption, and exponential

speed [3]. It is attainable by controlling the behavior of tiny

physical objects or microscopic particles including atoms,

electrons, and photons that transfer digital information.

In quantum computing, zero or one bit (one-bit) of

information is encoded using two orthogonal states of a

microscopic object known as quantum bit or qubit [4]. Having

both 0 and 1 simultaneously as its value is called

“superposition”. Also, they have a property known as

“entanglement” and is based on the changing of the state of one

Qubit also changes the state of another, even residing at a

distance. Qubits acquire both digital and analog nature that

brings the quantum computers into tremendous computational

power [5]. Several quantum algorithms have already been

developed, Grover’s algorithm for searching and Shor’s

algorithm for factoring large numbers in popular [6].

TABLE 1: DIFFERENCES BETWEEN QUANTUM COMPUTING AND

CLASSICAL COMPUTING

Quantum Computing

Classical Computing

Calculates with Qubits, that can

have values 0 o1 or both

simultaneously

Calculates with transistors,

that can have values either 0

or 1

Power increases exponentially in

proportion to the number of Qubits

Power increases linearly with

the number of transistors

Have high error rates

Have lower error rates

Operates at close to absolute zero

temperature

Operates at room temperature

Much secured to work with

Less secured to work with

Suited for big/complex tasks, such

as-optimization problems, data

analysis and simulations

Suited for everyday

processing tasks

Quantum programming languages are essential to translate

complex ideas into instructions to be executed by a quantum

computer. They facilitate the discovery and development of new

quantum algorithms, as well as executing the existing ones [7].

There is a number of key differences between Quantum

Computing and Classical Computing and provided in Table 1.

Quantum algorithms are already being applied in a variety of

industries including healthcare, finance, manufacturing,

cybersecurity, and blockchain. Optimization problems for

scheduling and route planning, search algorithms, sampling and

pattern matching, quantum encryption are a few of them. In

healthcare, accelerating drug discovery, drug design, optimizing

therapy/treatment, probable time to market new drugs are

possible due to Quantum Computing in healthcare industries.

Drafting trading strategies and detecting market instability for

financial services seems to be plausible because of it too.

Besides, advertisements strategy and product marketing,

software verification, and validation are much easier with

emerging Quantum Computing.

The main motivations for this study are the supreme nature

of quantum computing, its advantages in solving real-world

problems much more efficiently than classical computing and

identifying open challenges of this emerging research field. The

contribution of this paper is two-folds:

• We conduct a systematic review and present the progress of

quantum computing.

• We identify related frameworks, tools, and platforms that

facilitate quantum computing.

• We discuss the recommendation and future work in the area

of our study.

The rest of the paper is organized as follows: In Section II,

we discuss related work for Quantum Computing evolution and

Tools followed by research methodology in Section III. Section

IV provides details of various platforms and tools for Quantum

Computing followed by explaining circuit simulators of

quantum computing in Section V. Section VI explains tools and

software while Section VII discusses the current situation of the

field and its challenges followed by providing recommendations

for future research in Section VIII. Finally, Section IX concludes

the paper.

II. RELATED WORK

Quantum computing is trying to optimize algorithms in

various fields of computers by implementing and harnessing the

power of qubits in a quantum environment or computer.

Quantum evolutionary algorithm (QEA) and the divide-and-

conquer idea of cooperative coevolution evolutionary algorithm

(CCEA) are used to overcome the low solution efficiency,

insufficient diversity in the later search stage, slow convergence

speed and a higher search stagnation possibility of differential

evolution (DE).

Six algorithms in solving six test functions from CEC’08

under the dimensions of 100, 500 and 1000 resulted in an

improved differential evolution (HMCFQDE) with higher

convergence accuracy and stronger stability [8]. Quantum

evolutionary concepts were executed by quantum superposition

and entanglement which showed a logarithmic growth rate of

the number of evaluations of fitness functions needed to identify

a sufficiently accurate solution, and the depth of its quantum

circuits is O (1) with a significant impact on the effect of

quantum noise on computation.

Google’s classical PageRank algorithm has also explored its

quantum implementations with the growing content being

uploaded online [9]. The Quantum PageRank algorithm was

simulated on a six node web network for observation against the

PageRank algorithm. Quantum PageRanks were able to have

faster stabilization and consistent PageRank ordering by adding

a little noise during the computation of the Kossakowski-

Lindblad master equation.

M. Bidlo and P. Zufan [10] performed a comparative study

on the evolutionary design of quantum operators. Genetic

Algorithm and Evolution Strategy are applied, each in four

different setups, and evaluated on three case studies: the 2-qubit

Controlled-NOT gate, 3-qubit entanglement operator and 4-

qubit detector of an element with the maximum amplitude. The

newly applied QR decomposition achieved 100% success in 3-

qubit entanglement using both GA and ES and the best statistical

evaluation in case of the 4-qubit operator. Quantum computing

has superior computational strengths than the classical computer

and NP-hard problems have been tackled with this method.

Graph partitioning (NP-Hard) graph problem has been

solved by U. Chukwu et al. [11] utilizing two quantum-ready

methods of QUBO (quadratic unconstrained binary

optimization) and constrained-optimization sampler. Both

approaches often delivered better partition than the purpose-

built classical graph partitioners.

Stuart M. Harwood et al. [12] focus on variational quantum

eigensolver (VQE) which is a hybrid quantum-classical

algorithm. The authors adopted variational adiabatic quantum

computing (VAQC) to propose an improved VQE method that

continuously parameterized Hamiltonian via the quantum

circuit. The proposed technique VAQC has the ability to

successfully find good initial circuit parameters to initialize

VQE. The method was evaluated with two examples from

quantum chemistry combined with other techniques that provide

more accurate solutions than conventional VQE, for the same

amount of effort.

S. Boyapati, S. R. Swarna and A. Kumar [13] evaluated the

performance of a quantum computed prediction mode using

Quantum Neural Networks (QNN). This computational

mechanism using quantum computing and the neural network

will track the live operations and form the dynamic route

changes in the real-time scenario. This real-time scenario

worked with a 95% accuracy rate with its accuracy differing

based on the number of connecting nodes being considered.

Rosa M. Gil Iranzo et al. [14] addresses the limitations of

quantum computing interfaces that facilitate learning the

emerging paradigm. The authors proposed a layer to create

proper learning environments for performing calculations

without facilitating the understanding of the principle of

quantum computing concepts. The proposed work focuses on

Human-centered computing that shall facilitate various levels

including high school, university, and the research level. This

research is novel around the domain of design of quantum

computing interfaces integrating science and technology.

III. RESEARCH METHODOLOGY

The systematic literature review [15], [16] has been

conducted to find the current innovations that are either

completely new or modification of existing approaches for the

study on the Evolution of Quantum Computing, depicted in

Figure 1. A “Search Process” was implemented to acquire

research papers that address our topic of study. Thus, specific

search strings were applied during our analysis in scientific

databases which contained the keywords, “Quantum

Computing”, “Quantum Computing Evolution” and “Quantum

Computing Tools”.

Figure 1: Systematic Literature Mapping Process [17]

The scientific databases that were used for procuring these

papers including: (i) IEEE Xplore, (ii) ScienceDirect, (iii) ACM,

(iv) Springer Link, and (v) ResearchGate. We adopt a screening

process to find the most relevant papers by studying the paper

title followed by reading and understanding the abstract and

conclusion from screened papers.

TABLE II: GENERALIZED TABLE FOR SEARCH CRITERIA

Scientific Database

Initial Keyword Search

Total Inclusion

IEEE Xplore

109

4

ScienceDirect

50

2

ACM

50

2

Springer Link

25

0

Research Gate

17

0

Total

251

8

An exclusion and inclusion process based on (i) duplicate

papers (ii) full-text availability, and (iii) papers that are not

related to Quantum Computing was conducted to prune off

research papers that had aspects that were not related to our

literature review as well as duplicates that appeared during the

initial search. Table III. displays the details of the inclusion and

exclusion process.

TABLE IIII: OVERVIEW OF EXCLUSION AND INCLUSION

Category

Condition (Exclusion)

Condition (Inclusion)

Title

Does not include inclusion

topics

Quantum Computing

Evolution, Tools, Methods

Duplicate

Papers

Similar papers in multiple

scientific databases

Papers are not duplicated in

different scientific databases

Relativity

Studies that do not cover

expected domain

Proposed approaches reflect

Quantum Computing

Text

Availability

Papers are not available

fully and not in English

Papers that are available in

the full format & in English

Firstly, the filtration procedure had a time constraint that

allowed research papers published from the years 2016 to 2022.

Furthermore, additional filters were placed in each database to

narrow our search of relevant research materials. IEEE Xplore

included Conferences and Journals while ScienceDirect

required us to select Computer Science as the subject area and

research articles for article type. Springer Link and Research

Gate did not provide any unique or relevant topics of research.

We also included ACM scientific database where we included 2

papers for this study. Total 251 research papers were found

during the initial search but an in-depth screening process that

accounted for the publication title, abstract, experimental results

and conclusions shortened the list to 8 papers for our study.

IV. PLATFORM USED FOR QUANTUM COMPUTING

Quantum Platform (QP) or Quantum Computer Platform

(QCP) is a family of lightweight, open-source software

frameworks for building responsive and modular real-time

embedded applications in Quantum Computing [18]. Quantum

Computer Platform consists of two layers: Quantum Computing

Layer and Classical Computing Layer [19], [20] depicts in

Figure 2. In this section, we present our findings on platform

used for quantum computing with a research question- what are

the platforms used for quantum computing in the literature?

Figure 2: Quantum Computer Platform Architecture

A. The Quantum Computing Layers

An optimal set of hyperparameters allows performance

improvement as well as avoid performance issues like

overfitting. The Quantum hardware covers Qubits which are

surrounded by superconducting loops for the physical

realization of Qubits. It also consists of the internally connected

circuitry for Qubit control operations. Quantum Processing Unit

includes Quantum registers, logic gates, and memory. The

Quantum-Classical Interface houses the required hardware and

software in order to provide interfacing between the classical

computers and a Quantum Processing Unit (QPU). Lastly, the

Classical Computing Layer includes the final components-

Quantum Programming environment, Cloud data Centre and

Business Applications.

B. Characteristics of Quantum Computer Platform

• Low-level Programming: The Quantum Computers

currently in use are built on low-level programming.

They are based on quantum logical gates and handle

computational steps to execute in QPU.

• Heterogeneous: In QCP the technical specifications are

heterogeneous in nature for both software and hardware.

Some examples of QCP (or QP) are IBM, Microsoft, D-

wave, Google.

• Remote software development and deployment: All the

QCP vendors provide Quantum Computing software

development frameworks for leveraging quantum

processors that can only be accessed remotely from the

cloud. Only a small part of the programming tool stack

is deployed on the local machines. So, the programmers

access quantum software remotely for development and

testing.

• Quantum algorithms: The popular algorithms help in

gaining speed and communicating with other computing

tasks that are running on QCP. Additionally,

programmers need to either identify or design suitable

algorithms to solve the problems in hand.

• Portability of Software: The software developed by the

QCP owners are currently native in nature. This software

always follow its own standards, proprietary

programming API and predefined tools. Examples of

software will be found in the next segments of this paper.

V. CIRCUIT SIMULATOR FOR QUANTUM COMPUTING

The working procedure of a quantum circuit is shown with

the following diagrams of a simulator called “Quirk” [21], one

of the most used simulators for Quantum Computing. In the

circuit of Figure 3, there are two |0⟩|0⟩ qubits. When the gates

are dragged onto these circuits the output changes accordingly

[22].

The Hadamard gate or H-gate [23] is a quantum logic gate.

It redistributes the probability of all the input lines. As a result,

the output lines have an equal chance of being 0 and 1. Dragging

an H-gate onto one of the |0⟩|0⟩ circuits from Figure 3 will give

the circuit in Figure 4. Implementing the H-gate in this circuit

the output has a 50% chance of being measured ON, or 1.

Figure 3: Quantum Circuit with Quirk

Adding a new gate to the circuit of Figure 3 will give the

circuit in Fig. 4. The new gate is called the Pauli X gate, classical

the quantum equivalent of the NOT gate. This gate flips the input

state, so a 0 as input becomes 1 as output, and vice versa. From

the circuit of Figure 5 that is visible that the chance of measuring

a 1 is 100%. Some common quantum logic gates with their

associated matrices are shown in Table IV below.

Figure 4: Use of Hadamard Gate on the circuit

Figure 5: Use of Pauli's X-gate on the circuit

TABLE IV: COMMON QUANTUM LOGIC GATES WITH THEIR ASSOCIATED

MATRICES

Gate

Matrix

Pauli-X

"

0 1

1 0

"

Pauli-Y

"0 −𝑖

𝑖 0"

Pauli-Z

"1 0

0 −1"

Hadamard (H)

1

√

2

"

1 1

1 −1

"

Phase (S,P)

"1 0

0 𝑖"

π/8 (T)

"1 0

0 𝑒!"/$ "

Controlled NOT (CNOT,

CX)

*

1 0 0 0

0 1 0 0

0 0 0 1

0 0 1 0

*

Controlled Z (CZ)

*1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 −1*

SWAP

*1 0 0 0

0 0 1 0

0 1 0 0

0 0 0 1*

Toffoli(CCNOT, CCX,

TOFF)

*

*

*

1 0 0 0 0 0 0 0

0 1 0 0 0 0 0 0

0 0 1 0 0 0 0 0

0 0 0 1 0 0 0 0

0 0 0 0 1 0 0 0

0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 1

0 0 0 0 0 0 1 0

*

*

*

VI. TOOLS AND SOFTWARE FOR QUANTUM COMPUTING

Some of the tools and software used for Quantum

Computing are discussed in this section with a research

question, what are the tools and software are being used for

quantum computing in the literature?

A. Cirq

Cirq [24], [25] is an open-source Python library for writing,

manipulating and optimizing Noisy Intermediate Scale

Quantum (NISQ) circuits, and also for running them against

quantum computers and simulators illustrate in Figure

6. Moreover, it can be used with OpenFermion-Cirq which is a

platform for developing quantum algorithms for chemistry

problems. Cirq is not an official Google product, but Google AI

Quantum Team is promoting it.

B. TensorFlow Quantum

TensorFlow Quantum (TFQ) [26] is a quantum machine

learning library that is being used for prototyping of hybrid

quantum-classical machine learning models by Google illustrate

in Figure 7. It works with Cirq [27] to provide quantum

computing primitives compatible with existing TensorFlow

APIs, along with high-performance quantum circuit simulators.

Figure 6: A general overview of Cirq

Figure 7: How TensorFlow Quantum works with Cirq

C. ProjectQ

ProjectQ [28] is an open-source software framework that

allows users to implement quantum programs in Python using a

powerful and intuitive syntax (Fig 8). After that it can translate

these programs to any type of back-end, either a simulator

running on any classical computer or an actual quantum chip

including the IBM Quantum Experience platform.

D. CirqProjectQ

CirqProjectQ [29] is a port between ProjectQ and Cirq that

provides two main functions. As the first function, it has a

Circuit Simulators &

Quantum Cloud Service

Cirq

(programming framework)

Research Libraries and Tools

(OpenFermion, TensorFlow Quantum,

ReCirq, Pennylane etc.)

Cirq

Local simulator

(20-30 Qubits)

Quantum

Hardware

(not

currently

available

to general

users)

Quantum Engine (access

by invitation only)

ProjectQ backend that converts a ProjectQ algorithm to a cirq

circuit. Secondly, it can decompose ProjectQ common gates to

native Xmon gates to simulate a Google quantum computer with

ProjectQ.

Figure 8: Working procedure of ProjectQ

E. Microsoft Quantum Development Kit

Microsoft Quantum Development Kit [30], [31] appears to

supercede their earlier LIQUi|>software. This kit features a new

quantum programming language Q#. It works with integrating

the Visual Studio development environment (Figure 9 and

Figure 10).

Figure 9: Working procedure of Microsoft Quantum Development

Kit

F. IBM Quantum Experience

IBM’s 5 qubit gate-level quantum processor on the web

allows the users to apply to get access to it. The IBM Quantum

Experience [32], [33] website shows four modules, a short

tutorial with instructions to use it, a quantum composer to

configure quantum gates for the qubits, a simulator to simulate

the configuration before running it on the actual machine, and

finally access to the machine itself to run the configuration and

view the results (Figure 11). It has an associated software API

called QISKIT.

Figure 10: IDE of Microsoft Quantum Development Kit (integrated

with Microsoft Visual Studio)

Figure 11: How IBM Quantum Experience works

G. Rigetti Forest and Cloud Computing Services (QCS)

The Rigetti Forest suite [34], [35] consists of a quantum

instruction language Quil, an open source Python library pyQuil,

a library of quantum programs called Grove and a simulation

environment called QVM (Quantum Virtual

Machine). QCS provides a virtual classical computing

environment alongside the Rigetti quantum hardware. It comes

pre-configured with Rigetti’s Forest SDK and provides the users

with a single access point to the QVM and QPU backends.

H. CAS-Alibaba Quantum Computing Laboratory

The CAS-Alibaba Quantum Computing Laboratory [36] has

built several superconducting quantum computers. Their

hardware systems are available through an online interface for

Quantum

Development

Kit

Quantum

Computer

Topological

Qubit

the users to write quantum circuits, execute them, and download

the results over the cloud using a GUI.

I. Quantum Computing Playground

The Quantum Computing Playground [37] is a Chrome

Experiment or web app (Figure 12) uses WebGL to simulate up

to 22 qubits on a GPU. Inside it the users get a basic IDE to

write, compile and run the code; along with some example

algorithms (Grover’s, Shor’s). Also, a debugger and 3D

quantum state visualization tool are there, so users can see

what’s going on inside the little quantum computer. QScript is

the programming language used here, and it is similar to Bash-

like scripting languages.

Figure 12: How Quantum Computing Playground works (simulating

the example of Shor's algorithm)

J. Strawberry Fields

Strawberry Fields [38] is an open-source quantum

programming architecture for quantum machine learning depicts

in Figure 13.

Figure 13: Display How Strawberry Fields works

It uses Python language and consists of a full-stack library

for design, simulation, optimization and quantum machine

learning of several paradigmatic algorithms, such as-

teleportation, (Gaussian) boson sampling, instantaneous

quantum polynomial, Hamiltonian simulation and variational

quantum circuit optimization.

K. Wolfram Quantum Framework

The Wolfram Quantum Framework has more than 5000

built-in options to work on the quantum functions and objects

using Wolfram language [39]. The framework provides

Wolfram notebook to its users with full integration to

Mathematica and Wolfram language, where Mathematica is a

software system with built-in libraries for machine learning,

statistics etc. to develop and simulate several algorithms.

VII. CURRENT SITUATION OF THE FIELD AND ITS CHALLENGES

Today’s Quantum Computers take up an entire room, but

their capabilities are all really small-scale till now. They possess

less than 100 Qubits each which does not seem enough for the

tasks they are up to. Currently, the Quantum Computer with the

highest number of Qubits is China's Zuchongzhi with 66 Qubits

[40], [41]. It is able to perform a sampling task in 1.2 hours that

would take eight years for a Classical Computer to complete. At

present, about 46 countries are engaged in national or

international Quantum research works and developments. Most

of these actions are found to happen in academia and industry

[42], [43].

Lack of good software leads to technological challenges in

Quantum Computation including limited qubit connectivity, too

low gate fidelities, and the requirement of large amounts of

qubits for error correction. Lack of collaboration and exchange

between industry and academia is also a major issue in the

advancement of Quantum Computing. Furthermore, such

computers operate at temperatures close to zero, and

maintaining such a low temperature is always a big challenge.

Today, computers with 70 Qubits fall short of the requirement

of one million Qubits to make economically feasible and viable

Quantum Computers.

VIII. RECOMMENDATIONS FOR FUTURE RESEARCH

Cloud-oriented Quantum Computing has the potential to

overtake future business initiatives and technologies including

cryptography, machine learning (ML), and artificial intelligence

(AI) [44]–[47]. It seems plausible because of the almost

unlimited memory spaces available in clouds. Besides, shared

hardware could be proved helpful in solving complex tasks with

Quantum algorithms but by using a Classical Computer as its

base.

Looking at the possibilities, Quantum AI tools may provide

the world with autonomous weapons and mobile platforms [48]–

[50]. For example, drones made with Quantum AI tools can

achieve heightened sensing, navigation, and positioning options

in GPS-denied areas, as well as altering the course of operation

to avoid enemy countermeasures.

In addition, a new internet possibility with Quantum devices

emerged called the Quantum internet, which is separate from the

internet and links Quantum devices together using

entanglement. It significantly increases the connectivity,

security, and speed of the internet and shows the potential of the

super-secure communication infrastructure that protects

Quantum internet connected devices from cyberattacks to serve

in the field of cryptography. For instance, some scientists in the

Netherlands entangled three one-qubit devices in this manner,

and they successfully communicated and stored information in

a theoretically unhackable manner.

Moreover, Quantum cryptography, ML and AI tools can be

combined to improve intelligence service and its analysis [51],

[52]. Such intelligence services are supposed to be able to break

2048-bit RSA encryption in 8 hours or even less- a task that

would require the world’s fastest supercomputers around 300

trillion years to complete with brute-force methods. Quantum

computers might demand almost 20-million Qubits to perform

it. Advances in this field show possibility of such machines in

25 years. There is a chance of adversarial use of such computing

which can risk national and international security if advances in

Quantum decryption outrun advances in Quantum encryption.

With the advancement of Quantum Computing, ML and AI

problems could be solved in a practical amount of time- reduced

from hundreds of thousands of years to a few seconds.

Although there are many challenges in quantum computing

and accessibility in quantum devices, we study quantum

machine learning for software supply chain attacks [53]. In a

separate study, we also investigate quantum cybersecurity where

we discuss the threats, risks, and opportunities [54].

IX. CONCLUSION

Quantum Computing harnesses the phenomena of quantum

mechanics to solve complex problems that today's most

powerful supercomputers cannot solve. In this paper, we

reviewed the evolution of Quantum Computing where

tremendous efforts and progress mark a significant milestone in

solving real-world problems in recent years. We also present the

progress of various frameworks, tools, software, and platforms

that facilitate quantum computing. Finally, we discussed the

current essence, challenges, and provide a research scope for

future research. The findings of our study indicates that scores

of frameworks, tools and platforms are emerged in the past few

years, improvement of currently available facilities would

exploit the research activities in the quantum research

community.

ACKNOWLEDGEMENT

The work is partially supported by the U.S. National

Science Foundation Awards #2100115, #1723586 and

#1723578, National Institute of Health STTR

Grant#R41GM146313 and SunTrust Fellowship Award. Any

opinions, findings, and conclusions or recommendations

expressed in this material are those of the authors and do not

necessarily reflect the views of the National Science

Foundation, National Institute of Health and SunTrust

Fellowship Award.

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